pkg/R/computeGridLambda.R

   1 #' computeGridLambda
   2 #'
   3 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
   4 #'
   5 #' @param phiInit value for phi
   6 #' @param rhoInit   value for rho
   7 #' @param piInit    value for pi
   8 #' @param gamInit value for gamma
   9 #' @param X matrix of covariates (of size n*p)
  10 #' @param Y matrix of responses (of size n*m)
  11 #' @param gamma power of weights in the penalty
  12 #' @param mini minimum number of iterations in EM algorithm
  13 #' @param maxi maximum number of iterations in EM algorithm
  14 #' @param tau threshold to stop EM algorithm
  15 #'
  16 #' @return the grid of regularization parameters
  17 #'
  18 #' @export
  19 computeGridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y,
  20     gamma, mini, maxi, tau, fast=TRUE)
  21 {
  22     n = nrow(X)
  23     p = dim(phiInit)[1]
  24     m = dim(phiInit)[2]
  25     k = dim(phiInit)[3]
  26
  27     list_EMG = EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi,
  28         gamma, lambda=0, X, Y, tau, fast)
  29     grid = array(0, dim=c(p,m,k))
  30     for (i in 1:p)
  31     {
  32         for (j in 1:m)
  33             grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
  34     }
  35     sort( unique(grid) )
  36 }